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Bayesian inference for spatio-temporal spike-and-slab priors
[article]
2017
arXiv
pre-print
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We generalize the spike-and-slab prior distribution to encode a priori correlation of the support of the solution in both space and time by imposing a transformed Gaussian process on the spike-and-slab ...
An expectation propagation (EP) algorithm for posterior inference under the proposed model is derived. For large scale problems, the standard EP algorithm can be prohibitively slow. ...
Figures 16(g)-(h) show the same plots for the signal x 2 .
Bayesian Inference for Spatio-temporal Spike-and-Slab Priors
. We used the SPM8 software (Ashburner et al., 2010) .
. ...
arXiv:1509.04752v3
fatcat:hssscrspp5hu3pprd2jeet7t6a
Spatio-Temporal Structured Sparse Regression With Hierarchical Gaussian Process Priors
2018
IEEE Transactions on Signal Processing
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This paper introduces a new sparse spatio-temporal structured Gaussian process regression framework for online and offline Bayesian inference. ...
It is shown that it achieves 15% improvement of the F-measure compared with the alternating direction method of multipliers, spatio-temporal sparse Bayesian learning method and one-level Gaussian process ...
For spike and slab priors a spatio-temporal structure is modelled with a one-level Gaussian processes prior [25] , where the prior is imposed on all locations of non-zero components together. ...
doi:10.1109/tsp.2018.2858207
fatcat:6mc6ps35krbwrghxr2ptpqk3aq
Structured Sparse Modelling with Hierarchical GP
[article]
2017
arXiv
pre-print
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It incorporates the structural assumptions based on a hierarchical Gaussian process prior for spike and slab coefficients. ...
In this paper a new Bayesian model for sparse linear regression with a spatio-temporal structure is proposed. ...
One of the Bayesian approaches to force the coefficients being zeros is the spike and slab prior [3] : each component is modelled as a mixture of spike, that is the delta-function in zero, and slab, that ...
arXiv:1704.08727v1
fatcat:abjcxoqutrak3d44w6mj5chrvy
Spatio-temporal Spike and Slab Priors for Multiple Measurement Vector Problems
[article]
2015
arXiv
pre-print
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We propose a probabilistic model that takes this structure into account by generalizing the structured spike and slab prior and the associated Expectation Propagation inference scheme. ...
We are interested in solving the multiple measurement vector (MMV) problem for instances, where the underlying sparsity pattern exhibit spatio-temporal structure motivated by the electroencephalogram ( ...
CONCLUSION & OUTLOOK We extended the structured spike and slab prior and the associated Expectation Propagation inference scheme to cope with smooth temporal evolution of the sparsity pattern. ...
arXiv:1508.04556v1
fatcat:nrpvdi65dzcbfhlwvjoub6uuji
NPBayes-fMRI: Non-parametric Bayesian General Linear Models for Single- and Multi-Subject fMRI Data
2017
Statistics in Biosciences
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The modeling approach is based on a spatio-temporal linear regression model that specifically accounts for the between-subjects heterogeneity in neuronal activity via a spatially informed multi-subject ...
non-parametric variable selection prior. ...
A spiked non-parametric prior is imposed on the coefficients β iν j , i.e., a spike-and-slab prior where the slab distribution is modeled as a non-parametric prior, as β iν j |γ iν j , G i ∼ γ iν j G i ...
doi:10.1007/s12561-017-9205-0
fatcat:dgd74eetmjee3hlcco5lcllisi
Bayesian uncertainty quantification for data-driven equation learning
[article]
2021
arXiv
pre-print
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Equation learning aims to infer differential equation models from data. ...
We generate noisy data using a stochastic agent-based model and combine equation learning methods with approximate Bayesian computation (ABC) to show that the correct differential equation model can be ...
Acknowledgements: The authors would like to thank the referees for their comments. ...
arXiv:2102.11629v4
fatcat:dpcuqkqmobeghcix5onvcnogfe
Quantifying the Economic Impact of Extreme Shocks on Businesses using Human Mobility Data: a Bayesian Causal Inference Approach
[article]
2020
arXiv
pre-print
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However, surveys often suffer from high cost and long time for implementation, spatio-temporal sparsity in observations, and limitations in scalability. ...
Recently, large scale human mobility data (e.g. mobile phone GPS) have been used to observe and analyze human mobility patterns in an unprecedented spatio-temporal granularity and scale. ...
The work of T.Y. and S. V. ...
arXiv:2004.11121v1
fatcat:b7ry3uhdtzf3rldmeu5ntxpv3q
Bayesian statistics and modelling
2021
Nature Reviews Methods Primers
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This Primer describes the stages involved in Bayesian analysis, from specifying the prior and data models to deriving inference, model checking and refinement. ...
We discuss the importance of prior and posterior predictive checking, selecting a proper technique for sampling from a posterior distribution, variational inference and variable selection. ...
Spike-and-slab variable selection priors that incorporate structural information on the brain have been investigated within a wide class of spatio-temporal hierarchical models for the detection of activation ...
doi:10.1038/s43586-020-00001-2
fatcat:ezphge7kzzdwbmg37t3em7s7ui
Quantifying the economic impact of disasters on businesses using human mobility data: a Bayesian causal inference approach
2020
EPJ Data Science
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However, surveys often suffer from high cost and long time for implementation, spatio-temporal sparsity in observations, and limitations in scalability. ...
Recently, large scale human mobility data (e.g. mobile phone GPS) have been used to observe and analyze human mobility patterns in an unprecedented spatio-temporal granularity and scale. ...
Acknowledgements We thank Safegraph for preparing the mobile phone GPS data used in this study. ...
doi:10.1140/epjds/s13688-020-00255-6
fatcat:t6cjbbcuorbcfpstg7ws22a564
A Bayesian Variable Selection Approach Yields Improved Detection of Brain Activation From Complex-Valued fMRI
2018
Journal of the American Statistical Association
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We develop models with complexvalued spike-and-slab priors on the activation parameters that are able to combine the magnitude and phase information. ...
We present a new Bayesian variable selection approach for detecting brain activation at the voxel level from CV-fMRI data. ...
Adding spatio-temporal structure that can better describe the data could potentially lead to further improved results, but would also lead to more computationally intensive models that may be not be feasible ...
doi:10.1080/01621459.2018.1476244
fatcat:zj5pjupy7ngcjjqu5cu7e2v7de
A Truncated EM Approach for Spike-and-Slab Sparse Coding
[article]
2014
arXiv
pre-print
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We study inference and learning based on a sparse coding model with 'spike-and-slab' prior. ...
In applications to source separation we find that both approaches improve the state-of-the-art in a number of standard benchmarks, which argues for the use of 'spike-and-slab' priors for the corresponding ...
Acknowledgements Furthermore, we acknowledge support by the Frankfurt Center for Scientific Computing (CSC Frankfurt). ...
arXiv:1211.3589v3
fatcat:oa6bkopd4faeph6z77pgpga6ya
Regularizing Solutions to the MEG Inverse Problem Using Space-Time Separable Covariance Functions
[article]
2016
arXiv
pre-print
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We apply the method to both simulated and empirical data, and demonstrate the efficiency and generality of our Bayesian source reconstruction approach which subsumes various classical approaches in the ...
the number of time steps, m is the number of sources, and n is the number of sensors. ...
Acknowledgements This work was supported by the Academy of Finland grant numbers 266940 and 273475, the Netherlands Organization for Scientific Research (NWO) grant numbers 612.001.211 and 639.072.513, ...
arXiv:1604.04931v1
fatcat:zfssqpcu35cxjgrvvv7nsxd5rq
Spatio-Temporal Patterns in Portuguese Regional Fertility Rates: A Bayesian Approach for Spatial Clustering of Curves
2021
Journal of Official Statistics
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Here, we consider Bayesian hierarchical models with separable spatio-temporal dependence structure that can be estimated by borrowing strength from neighbouring regions and all years. ...
For this purpose, we use the local similarity of temporal patterns by developing a spatial clustering model based on Bayesian nonparametric smoothing techniques. ...
Wavelet coefficient at position m of resolution l, for kth covariate in rth cluster g rk (lm)
Appendix Bernoulli random variable for the spike-and-slab prior: 0 means b rk (lm) is a point mass at 0, ...
doi:10.2478/jos-2021-0028
fatcat:5ozf45wxc5gsrgtlg27rqjrp5u
Bayesian Joint Modeling of Multiple Brain Functional Networks
[article]
2019
arXiv
pre-print
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Conditional on these edge probabilities, connection strengths are modeled under a Bayesian spike and slab prior on the off-diagonal elements of the inverse covariance matrix. ...
The proposed Bayesian Joint Network Learning approach develops flexible priors on the edge probabilities involving a common intrinsic baseline structure and differential effects specific to individual ...
world and scale-free networks simulation scenarios, and additional details on the Stroop Task data analysis. ...
arXiv:1708.02123v2
fatcat:g54vkaxhhnbfhp2yqcwyj2chte
Semiparametric Functional Factor Models with Bayesian Rank Selection
[article]
2022
arXiv
pre-print
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The nonparametric factors are regularized with an ordered spike-and-slab prior that provides consistent rank selection and satisfies several appealing theoretical properties. ...
inference on the effective number of nonparametric terms--all with minimal additional computational costs. ...
Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation herein. ...
arXiv:2108.02151v3
fatcat:qtr24igr5bhd5jwqfpd4763k3m
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